> For a model it doesn’t seem that far fetched to make the leap from “I have the knowledge of what hacking is” and “I know how computer systems work” to just being able to exploit stuff anyways.
You'd be surprised. Cyber offense is a very narrow domain, especially given the extensive mitigations that are being used in modern systems. Leaving it out of post-training tasks unless you're specifically interested in it is the no-brainer approach.
Perhaps the gap is just too wide, but to use a more simple analogy. I see this like someone knowing everything about biology, simple mechanics, metallurgy, and human history but never hearing about an axe then assuming that person would never come up with the concept, use case of, or means to create an axe.
What confuses me about this analogy is that it seems to imply this is a matter of a knowledge-base and logical inferences. But the whole reason why LLMs are so successful is that the logical approach was abandoned in favor of a statistical one (and huge amounts of data, compute, and weights). The argument is precisely, that what is not in the training does not find its way into the probability distribution. To claim that LLMs reason is a very far stretch that I don't feel comfortable to let pass.
Output can appear logical without the model actually applying formal logic. If its training has associated tools with wood, and sharp edges with splitting, then a prompt containing those ideas can make “use a sharp-edged tool to split wood” a likely continuation even if that exact combination never appeared in training.
By asking follow-up questions and establishing an in-context vocabulary, users can steer output toward a more complete description. For example, “Let’s call that tool and ‘axe’. How might I hold it?”; the model’s learned associations with holding sharp knives carefully can make “by the handle, with the blade facing away from you” a likely response.
I’m confident you could arrive at a near perfect description of an axe this way without any references or depictions of an axe in the training data. I’d imagine the same applies to many cybsecurity/cyber offense concepts (but probably not all).
Will there be a book of secret knowledge that only the initiated will be allowed to see? And they will all swear an oath to never input it into an AI system?
And then we'd also do that for all kinds of potential terrorism, sex-crimes, political subversion, and evil advocacy?
It would be the "restricted section" like in the Hogwarts library?
Post-training is not about "secret knowledge": it's literally about training the model in a RL environment on very specific tasks (including, potentially, cyber exploitation). It's just as compute-intensive as pre-training these days, if not more (given that several checkpoints of the same pre-trained model are being released, showing sizeable gains in real-world capabilities for non-trivial productivity tasks).